Privacy For User Behavior Data

References

[1] R. E. Bucklin and C. Sismeiro, “Click here for internet insight: Advances in clickstream data analysis in marketing,” Journal of Interactive Marketing, vol. 23, no. 1, pp. 35– 48, 2009.

[2] R. Bose, “Advanced analytics: opportunities and challenges,” Industrial Management & Data Systems, vol. 109, no. 2, pp. 155–172, 2009.

[3] H. Chen, R. H. Chiang, and V. C. Storey, “Business intelligence and analytics: From big data to big impact.” MIS quarterly, vol. 36, no. 4, pp. 1165–1188, 2012.

[4] I. E. Akkus, R. Chen, M. Hardt, P. Francis, and J. Gehrke, “Nontracking web analytics,” in Proceedings of the ACM Conference on Computer and communications security (CCS), 2012, pp. 687–698.

[5] F. Roesner, T. Kohno, and D. Wetherall, “Detecting and defending against third-party tracking on the web,” in Proceedings of the 9 th USENIX conference on Networked Systems Design and Implementation, 2012.

[6] Directive 2009/136/ec of the european parliament and of the council. [Online]. Available: http://eur-lex.europa.eu/ LexUriServ/LexUriServ.do?uri=OJ:L:2009: 337:0011:0036:en:PDF

[7] Web tracking protection. [Online]. Available: http://www.w3. org/Submission/web-tracking-protection/

[8] V. Rastogi and S. Nath, “Differentially private aggregation of distributed time-series with transformation and encryption,” in Proceedings of the ACM International Conference on Management of Data (SIGMOD), 2010, pp. 735–746.

[9] B. Applebaum, H. Ringberg, M. J. Freedman, M. Caesar, and J. Rexford, “Collaborative, privacy-preserving data aggregation at scale,” in Proceedings of the 10th Privacy Enhancing Technologies Symposium (PETS), 2010, pp. 56–74.

[10] R. Chen, I. E. Akkus, and P. Francis, “SplitX: high-performance private analytics,” in Proceedings of the ACM Special Interest Group on Data Communication (SIGCOMM), 2013, pp. 315–326.

[11] R. Chen, A. Reznichenko, P. Francis, and J. Gehrke, “Towards statistical queries over distributed private user data,” in Proceedings of the 9th Symposium on Networked Systems Design and Implementation (NSDI), 2012.

[12] E. Shi, T.-H. H. Chan, E. G. Rieffel, R. Chow, and D. Song, “Privacypreserving aggregation of time-series data,” in Proceedings of the Network and Distributed System Security Symposium (NDSS), 2011.

[13] T. Jung, X. Mao, X.-y. Li, S.-J. Tang, W. Gong, and L. Zhang, “Privacy-preserving data aggregation without secure channel: multivariate polynomial evaluation,” in Proceedings of the 32 nd IEEE International Conference on Computer Communications (INFOCOM), 2013, pp. 2634–2642.

[14] D. Fiore, R. Gennaro, and V. Pastro, “Efficiently verifiable computation on encrypted data,” in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (CCS), 2014, pp. 844–855.

[15] T. Jung, X.-Y. Li, and M. Wan, “Collusion- tolerable privacy preserving sum and product calculation without secure channel,” IEEE Transactions on Dependable and Secure Computing (TDSC), vol. 12, no. 1, pp. 45– 57, 2015.

[16] C. Dwork, “Differential privacy: A survey of results,” in Proceedings of 5th International Conference on Theory and Applications of Models of Computation (TAMC), 2008, pp. 1–19.

[17] M. Hardt and S. Nath, “Privacy-aware personalization for mobile advertising,” in Proceedings of the ACM conference on Computer and Communications Security (CCS), 2012, pp. 662–673.

[18] C. Dwork, “Differential privacy,” in Proceedings of the 33rd International Colloquium on Automata, Languages and Programming (ICALP), 2006, pp. 1–12.

[19] C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of Cryptography, 2006, pp. 265–284.

[20] C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor, “Our data, ourselves: Privacy via distributed noise generation,” in Proceedings of 25th Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT), 2006, pp.486–503.

[21] S. Inusah and T. J. Kozubowski, “A discrete analogue of the laplace distribution,” Journal of statistical planning and inference, vol. 136, no. 3, pp. 1090–1102, 2006.

@article{1700374,
      author = {Yamunasai, Vardhani Peram, Bindu Sree Popuri},
      title = {Privacy For User Behavior Data},
      journal = {Iconic Research And Engineering Journals},
      year = {2018},
      volume = {1},
      number = {9},
      pages = {223-229},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1700374.pdf},
      abstract = {Tons of online user behavior data are being generated every day on the booming and ubiquitous Internet. Growing efforts have been devoted to mining the abundant behavior data to extract valuable information for research purposes or business interests. However, online users' privacy is thus under the risk of being exposed to third-parties. The last decade has witnessed a body of research works trying to perform data aggregation in a privacy-preserving way. Most of existing methods guarantee strong privacy protection yet at the cost of very limited aggregation operations, such as allowing only summation, which hardly satisfies the need of behavior analysis. In this paper, we propose a scheme PPSA, which encrypts users? sensitive data to prevent privacy disclosure from both outside analysts and the aggregation service provider, and fully supports selective aggregate functions for online user behavior analysis while guaranteeing differential privacy. We have implemented our method and evaluated its performance using a trace-driven evaluation based on a real online behavior dataset. Experiment results show that our scheme effectively supports both overall aggregate queries and various selective aggregate queries with acceptable computation and communication overheads.},
      month = {March},
  }